workshop-facilitation
关于
This skill provides structured facilitation for interactive workshops by guiding users through a step-by-step, multi-turn flow. It maintains consistent pacing with progress tracking and offers adaptive recommendations at decision points. Use it when you need to run guided sessions like product workshops that require clear options and predictable interruption handling.
快速安装
Claude Code
推荐npx skills add deanpeters/Product-Manager-Skills -a claude-code/plugin add https://github.com/deanpeters/Product-Manager-Skillsgit clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/workshop-facilitation在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Purpose
Provide the canonical facilitation pattern for interactive skills: one step at a time, with clear progress, adaptive recommendations at decision points, and predictable interruption handling.
Key Concepts
- One-step-at-a-time: Ask a single targeted question per turn.
- Session heads-up + entry mode: Start by setting expectations and offering
Guided,Context dump, orBest guessmode. - Progress visibility: Show user-facing progress labels like
Context Qx/8andScoring Qx/5. - Decision-point recommendations: Use enumerated options only when a choice is needed, not after every answer.
- Quick-select response options: For regular context/scoring questions, provide concise numbered answer options plus
Other (specify)when useful. - Flexible selection parsing: Accept
#1,1,1 and 3,1,3, or custom text, then synthesize multi-select choices. - Context-aware progression: Build on previous answers and avoid re-asking resolved questions.
- Interruption-safe flow: Answer meta questions directly (for example, "how many left?"), restate status, then resume.
- Fast path: If the user requests a single-shot output, skip multi-turn facilitation and deliver a condensed result.
Application
- Start with a brief heads-up on estimated time and number of questions.
- Ask the user to choose an entry mode:
1Guided mode (one question at a time)2Context dump (paste known context; skip redundancies)3Best guess mode (infer missing details and label assumptions)
- Run one question per turn and wait for an answer before continuing.
- Keep questions plain-language; include a short example response format when helpful.
- Show progress each turn:
Context Qx/8during context collectionScoring Qx/5during assessment/scoring
- Ask follow-up clarifications only when they materially improve recommendation quality.
- For regular context/scoring questions, offer quick-select numbered response options when practical:
- Keep options concise and mutually exclusive when possible.
- Include
Other (specify)if likely answers are open-ended. - Accept multi-select responses like
1,3or1 and 3.
- Provide numbered recommendations only at decision points:
- after context synthesis,
- after maturity/profile synthesis,
- during priority/action-plan selection.
- Accept numeric or custom choices, synthesize multi-select choices, and continue.
- If interrupted by a meta question, answer directly, then restate progress and pending question.
- If the user says stop/pause, halt immediately and wait for explicit resume.
- End with a clear summary, decisions made, and (if best guess mode was used) an
Assumptions to Validatelist.
Examples
Opening: "Quick heads-up: this should take about 7-10 minutes and around 10 questions. How do you want to start?
- Guided mode
- Context dump
- Best guess mode"
User: "2"
Facilitator: "Paste what you already know. I’ll skip answered areas and ask only what’s missing."
Decision point after synthesis:
- Prioritize Context Design (Recommended)
- Prioritize Agent Orchestration
- Prioritize Team-AI Facilitation
User: "1 and 3"
Facilitator: "Great. We’ll run Context Design first, with Team-AI Facilitation in parallel."
Common Pitfalls
- Asking multiple questions in the same turn.
- Offering recommendations after every answer (creates interaction drag).
- Using shorthand labels without plain-language questions.
- Hiding progress, so users don't know how much remains.
- Ignoring the user's chosen option or custom direction.
- Failing to label assumptions when running in best-guess mode.
References
- Use as the source of truth for interactive facilitation behavior.
- Apply alongside workshop skills in
skills/*-workshop/SKILL.mdand advisor-style interactive skills.
GitHub 仓库
相关推荐技能
content-collections
元Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。
polymarket
元这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。
creating-opencode-plugins
元该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。
sglang
元SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。
